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                <h1 id="主从库如何实现数据一致"><a href="#主从库如何实现数据一致" class="headerlink" title="主从库如何实现数据一致"></a>主从库如何实现数据一致</h1><h2 id="前置知识"><a href="#前置知识" class="headerlink" title="前置知识"></a>前置知识</h2><p>我们知道 Redis 有 AOF 和 RDB 两种持久化机制，如果 Redis 发生宕机，可以通过这两种机制恢复数据，从而保证尽量少丢失数据，提升可靠性。</p>
<p>不过使用了这两种机制依然存在服务不可用的问题，比如我们实际只运行了一个 Redis 实例，如果这个实例宕机了，他在恢复期间，是无法服务新来的数据存储请求的。</p>
<p>那我们总说的 Redis 具有高可用性是怎么回事呢？其实这包括两个方面：1、数据尽量少丢失，2、服务尽量少中断。AOF 和 RDB 保重了第一点，而对于第二点，Redis 的做法就是增加副本容量，将一份数据同时保存在多个实例上，即时有一个实例出现了故障，需要过一段时间才能恢复，其他实例也可以对外提供服务，不会影响业务使用。</p>
<p>多实例保存同一份数据，我们必须要解决一个问题，就是这多个副本之间的数据如何保持一致呢？数据读写操作可以发给所有的实例吗？实际上，Redis 提供了主从模式，以保证数据的一致，主从库之间采用的是读写分离的方式。</p>
<ul>
<li>读操作：主库、从库都可以接收。</li>
<li>写操作：首先到主库执行，然后，主库将写操作同步给从库。</li>
</ul>
<img src="https://cdn.jsdelivr.net/gh/kuangtf/PictureBed/img/image-20220327121005137.png" alt="image-20220327121005137" style="zoom:80%;" />

<p>那么，为什么要采用读写分离的方式呢？  </p>
<p>如果在上图中，不管是主库还是从库，都能接收客户端的写操作，那么，一个直接的问题就是：如果客户端对同一个数据（例如 k1）前后修改了三次，每一次的修改请求都发送到不同的实例上，在不同的实例上执行，那么，这个数据在这三个实例上的副本就不一致了（分别是 v1、v2 和 v3）。在读取这个数据的时候，就可能读取到旧的值。</p>
<p>如果我们非要保持这个数据在三个实例上一致，就要涉及到加锁、实例间协商是否完成修改等一系列操作，但这会带来巨额的开销，这对于 Redis 来说肯定是不能接受的。</p>
<p>而主从库模式一旦采用了读写分离，所有数据的修改只会在主库上进行，不用协调三个实例。主库有了最新的数据后，会同步给从库，这样，主从库的数据就是一致的。  </p>
<p>那么，主从库同步是如何完成的呢？主库数据是一次性传给从库，还是分批同步？要是主从库间的网络断连了，数据还能保持一致吗？下面我就带你一探究竟。</p>
<h2 id="主从库间如何进行第一次同步"><a href="#主从库间如何进行第一次同步" class="headerlink" title="主从库间如何进行第一次同步"></a>主从库间如何进行第一次同步</h2><p>当我们启动多个 Redis 实例的时候，它们相互之间就可以通过 replicaof（Redis 5.0 之前使用 slaveof）命令形成主库和从库的关系，之后会按照三个阶段完成数据的第一次同步。  </p>
<p>例如，现在有实例 1（ip：172.16.19.3）和实例 2（ip：172.16.19.5），我们在实例 2 上执行以下这个命令后，实例 2 就变成了实例 1 的从库，并从实例 1 上复制数据：  </p>
<pre class="line-numbers language-shell" data-language="shell"><code class="language-shell">replicaof <span class="token number">172.16</span>.19.3 <span class="token number">6379</span><span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>

<p>接下来就是主从库同步的三个阶段了，整体步骤如下图：</p>
<img src="https://cdn.jsdelivr.net/gh/kuangtf/PictureBed/img/image-20220327121012099.png" alt="image-20220327121012099" style="zoom:80%;" />

<p><strong>第一阶段</strong>是主从库间建立连接、协商同步的过程，主要是为全量复制做准备。在这一步，从库和主库建立起连接，并告诉主库即将进行同步，主库确认回复后，主从库间就可以开始同步了。  </p>
<p>具体来说，从库给主库发送 psync 命令，表示要进行数据同步，主库根据这个命令的参数来启动复制。psync 命令包含了主库的 runID 和复制进度 offset 两个参数。  </p>
<blockquote>
<p>runID，是每个 Redis 实例启动时都会自动生成的一个随机 ID，用来唯一标记这个实例。当从库和主库第一次复制时，因为不知道主库的 runID，所以将 runID 设为“？”  </p>
<p>offset，此时设为 -1，表示第一次复制。</p>
</blockquote>
<p>主库收到 psync 命令后，会用 FULLRESYNC 响应命令带上两个参数：主库 runID 和主库目前的复制进度 offset，返回给从库。从库收到响应后，会记录下这两个参数。  </p>
<blockquote>
<p>FULLRESYNC 响应表示第一次复制采用的全量复制，也就是说，主库会把当前所有的数据都复制给从库。</p>
</blockquote>
<p><strong>第二阶段</strong>，主库将所有数据同步给从库。从库收到数据后，在本地完成数据加载。这个过程依赖于内存快照生成的 RDB 文件。  </p>
<p>具体来说，主库执行 bgsave 命令，生成 RDB 文件，接着将文件发给从库。从库接收到RDB 文件后，会先清空当前数据库，然后加载 RDB 文件。这是因为从库在通过 replicaof命令开始和主库同步前，可能保存了其他数据。为了避免之前数据的影响，从库需要先把当前数据库清空。</p>
<p>在主库将数据同步给从库的过程中，主库不会被阻塞，仍然可以正常接收请求。否则，Redis 的服务就被中断了。但是，这些请求中的写操作并没有记录到刚刚生成的 RDB 文件中。为了保证主从库的数据一致性，主库会在内存中用专门的 replication buffer，记录RDB 文件生成后收到的所有写操作。  </p>
<p><strong>第三个阶段</strong>，主库会把第二阶段执行过程中新收到的写命令，再发送给从库。</p>
<p>具体的操作是，当主库完成 RDB 文件发送后，就会把此时 replication buffer 中的修改操作发给从库，从库再重新执行这些操作。这样一来，主从库就实现同步了。  </p>
<h2 id="主从级联模式分担全量复制时的主库压力"><a href="#主从级联模式分担全量复制时的主库压力" class="headerlink" title="主从级联模式分担全量复制时的主库压力"></a>主从级联模式分担全量复制时的主库压力</h2><p>经过上面主从库间的第一次数据同步操作，我们知道一次全量复制对于主库来说需要两个耗时操作：生成 RDB 文件和传输 RDB 文件。  </p>
<p>如果从库数量很多，而且都要和主库进行全量复制的话，就会导致主库忙于 fork 子进程生成 RDB 文件，进行数据全量同步。fork 这个操作会阻塞主线程处理正常请求，从而导致主库响应应用程序的请求速度变慢。此外，传输 RDB 文件也会占用主库的网络带宽，同样会给主库的资源使用带来压力。那么，有没有好的解决方法可以分担主库压力呢？  </p>
<p>我们可以使用“主 - 从 - 从”模式 来解决主库压力过大的问题，通过“主 - 从 - 从”模式将主库生成 RDB 和传输 RDB 的压力，以级联的方式分散到从库上。  </p>
<p>简单来说，我们在部署主从集群的时候，可以手动选择一个从库（比如选择内存资源配置较高的从库），用于级联其他的从库。然后，我们可以再选择一些从库（例如三分之一的从库），在这些从库上执行如下命令，让它们和刚才所选的从库，建立起主从关系。</p>
<pre class="line-numbers language-none"><code class="language-none">replicaof 所选从库的IP 6379<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>

<p>这样一来，这些从库就会知道，在进行同步时，不用再和主库进行交互了，只要和级联的从库进行写操作同步就行了，这就可以减轻主库上的压力，如下图所示：  </p>
<img src="https://cdn.jsdelivr.net/gh/kuangtf/PictureBed/img/image-20220327121020308.png" alt="image-20220327121020308" style="zoom:80%;" />

<p>一旦主从库完成了全量复制，它们之间就会一直维护一个网络连接，主库会通过这个连接将后续陆续收到的命令操作再同步给从库，这个过程也称为基于长连接的命令传播，可以避免频繁建立连接的开销。</p>
<p>但是这个过程中存在着风险点，最常见的就是网络断连或阻塞。如果网络断连，主从库之间就无法进行命令传播了，从库的数据自然也就没办法和主库保持一致了，客户端就可能从从库读到旧数据。下面我们就来看看主从库间网络断了怎么办？</p>
<h2 id="主从库间网络断了怎么办？"><a href="#主从库间网络断了怎么办？" class="headerlink" title="主从库间网络断了怎么办？"></a>主从库间网络断了怎么办？</h2><p>在 Redis 2.8 之前，如果主从库在命令传播时出现了网络闪断，那么，从库就会和主库重新进行一次全量复制，开销非常大。从 Redis 2.8 开始，网络断了之后，主从库会采用增量复制的方式继续同步。  </p>
<blockquote>
<p>全量复制是同步所有数据，而增量复制只会把主从库网络断连期间主库收到的命令，同步给从库。</p>
</blockquote>
<p>那么，增量复制时，主从库之间具体是怎么保持同步的呢？这里的奥妙就在于repl_backlog_buffer 这个缓冲区。我们先来看下它是如何用于增量命令的同步的。  </p>
<p>当主从库断连后，主库会把断连期间收到的写操作命令，写入 replication buffer，同时也会把这些操作命令也写入 repl_backlog_buffer 这个缓冲区。repl_backlog_buffer 是一个环形缓冲区，主库会记录自己写到的位置，从库则会记录自己已经读到的位置。  </p>
<p>刚开始的时候，主库和从库的写读位置在一起，这算是它们的起始位置。随着主库不断接收新的写操作，它在缓冲区中的写位置会逐步偏离起始位置，我们通常用偏移量来衡量这个偏移距离的大小，对主库来说，对应的偏移量就是 master_repl_offset。主库接收的新写操作越多，这个值就会越大。  </p>
<p>同样，从库在复制完写操作命令后，它在缓冲区中的读位置也开始逐步偏移刚才的起始位置，此时，从库已复制的偏移量 slave_repl_offset 也在不断增加。正常情况下，这两个偏移量基本相等。  </p>
<p>如下图所示：</p>
<img src="https://cdn.jsdelivr.net/gh/kuangtf/PictureBed/img/image-20220327121028501.png" alt="image-20220327121028501" style="zoom:80%;" />

<p>主从库的连接恢复之后，从库首先会给主库发送 psync 命令，并把自己当前的slave_repl_offset 发给主库，主库会判断自己的 master_repl_offset 和 slave_repl_offset之间的差距。  </p>
<p>在网络断连阶段，主库可能会收到新的写操作命令，一般来说，master_repl_offset会大于 slave_repl_offset。此时，主库只用把 master_repl_offset 和 slave_repl_offset之间的命令操作同步给从库就行  </p>
<p>主要流程如下图所示：</p>
<img src="https://cdn.jsdelivr.net/gh/kuangtf/PictureBed/img/image-20220327121036142.png" alt="image-20220327121036142" style="zoom:80%;" />

<p>注意： repl_backlog_buffer 是一个环形缓冲区，所以在缓冲区写满后，主库会继续写入，此时，就会覆盖掉之前写入的操作。如果从库的读取速度比较慢，就有可能导致从库还未读取的操作被主库新写的操作覆盖了，这会导致主从库间的数据不一致。</p>
<p>为了避免这个问题，我们可以调整 repl_back_size 参数，这个参数和所需的缓冲空间大小有关。缓冲空间的计算公式是：缓冲空间大小 = 主库写入命令速度 * 操作大小 - 主从库间网络传输命令速度 * 操作大小。在实际应用中，考虑到可能存在一些突发的请求压力，我们通常需要把这个缓冲空间扩大一倍，即repl_backlog_size = 缓冲空间大小 * 2，这也就是 repl_backlog_size 的最终值。</p>
<p>到此，我们的Redis 主从同步就结束了，你看懂了吗。</p>
<blockquote>
<p> 巨人的肩膀：</p>
<p>极客时间Redis核心技术与实战</p>
</blockquote>

                
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